Determinants of ventilatory instability and variability

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Abstract

This paper reviews the major mechanisms that can give rise to various forms of variability in the ventilatory pattern. First, an elevated controller gain, coupled with the presence of delays and response lags in the chemoreflex loops, can lead to instability in feedback control and give rise to periodic breathing. This form of ventilatory stability can be assessed quantitatively by employing the concept of ‘loop gain’. Several different methods of estimating loop gain from steady state or dynamic respiratory measurements are discussed. An inherently stable respiratory control system can also exhibit periodic behavior due to the influence of primary fluctuations in sleep–wake state and other physiological variables, such as cardiac output and cerebral blood flow. Self-sustained, irregular ventilatory fluctuations may be generated by nonlinear dynamic interactions between various components of the respiratory control system, such as the lung vagal afferents and the respiratory pattern generator, or through the propagation of stochastic disturbances around the chemoreflex loops.

Introduction

The terms ‘instability’ and ‘variability’ are commonly used to characterize similar states of dynamic behavior. Measurements derived from an unstable system are likely to show a high degree of variability, whereas a stable system is expected to exhibit dynamics of low variability. While these associations are intuitive and frequently valid, a closer examination of these notions reveals that the underlying mechanisms that they represent can be very different. Overt manifestations of instability and variability in respiratory control are easy to identify, for instance, during periodic breathing or Cheyne–Stokes respiration, bursts of large, vigorous breaths alternate with periods of apnea or hypopneic breaths. Spontaneous tidal breathing is generally regarded as representing the output of a ‘stable’ respiratory control system since the average level of ventilation remains relatively constant over an extended period of time. However, even under such circumstances, there is significant variability in ventilation from one breath to the next. Does substantial breath-to-breath variability necessarily indicate a respiratory control system that is ‘less stable’ than one in which ventilatory variability is much lower? Although the intuitive reply is ‘yes’, careful consideration, employing the concepts of control engineering to be developed in this paper, shows that the answer is not so straightforward.

A definition of an ‘unstable system’ applicable to ventilatory control but consistent with the terminology employed in engineering systems is that it is a system that does not revert to its original steady state level of ventilation (and original level of breath-to-breath variability), following a brief disturbance. However, ventilatory variability can also result from perturbations derived from other systems that interact with an inherently stable chemoreflex control system. The purpose of this article is to elaborate on the distinction between the notions of ‘stability’ and ‘variability’ by reviewing our current knowledge of the principal mechanisms that determine the dynamic behavior of the ventilatory control system. From a pragmatic standpoint, a determination of whether the observed variability in question results from an inherently unstable system requires a means of quantifying stability. For this reason, we have devoted a significant portion of this paper to reviewing some of the major experimental and analytical approaches that have been adopted for assessing ventilatory control stability, with emphasis on the modeling aspects of this topic. For more extensive coverage of the physiological mechanisms underlying ventilatory variability and periodic breathing, the reader is referred to other reviews that have recently appeared in the literature (Cherniack and Longobardo, 1994, Bruce and Daubenspeck, 1995, Dempsey et al., 1996, Khoo, 1999a).

Section snippets

Factors contributing to ventilatory variability

We begin by considering a simplified model of the chemoreflex control of ventilation, as illustrated in the schematic block diagram of Fig. 1. The way in which arterial PCO2 (PaCO2) is regulated here is a classic example of negative-feedback control. Fluctuations in PaCO2 stimulate the chemoreflexes to produce ventilatory adjustments that act to restore PaCO2 back towards its original equilibrium level. Thus, a transient episode of hyperpnea would lead to lower PaCO2 which, acting through the

Ventilatory variability arising from feedback instability

A considerable body of evidence from human and animal studies has accumulated over the years to support the notion that, in most cases, periodic breathing is the result of unstable respiratory control. For instance, it has long been recognized that hypoxia, imposed through either inhalation of low-O2 mixtures or ascent to altitude, induces periodic breathing (Mosso, 1898, Douglas and Haldane, 1909, Berssenbruge et al., 1983, Lahiri et al., 1983, Waggener et al., 1984a, White et al., 1987). This

Methods for assessing stability

Given that ventilatory variability may reflect the behavior of an inherently unstable chemoreflex control system or the response of a highly stable system to external perturbations or internal parameter fluctuations, how does one go about distinguishing one type of variability from another? The use of a simulation model may be helpful to some extent, since it allows the researcher to determine whether certain observational details are consistent with the hypothesis at hand. For instance, the

Nonlinear dynamics and ventilatory variability

The existence of nonlinearities and feedback delays in the ventilatory control system suggests that, in principle at least, some of the dynamic interactions might lead to the generation of sustained aperiodic fluctuations even in the absence of stochastic noise inputs. There are many respiratory control mechanisms that could give rise to nonlinear dynamic interactions. Upper airway patency, for instance, is influenced by a multitude of factors, including chemical drive, changes in state and

Acknowledgements

Preparation of this manuscript was supported in part by National Institutes of Health grants RR-01861 and HL-58725.

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